Edinburgh Research Explorer

Dr Thanasis Tsanas

Chancellor's Fellow

Area of Expertise

Research expertiseTime-series analysis, pattern recognition, signal processing, statistical machine learning

My research in a nutshell

I develop and apply novel time-series, signal processing, pattern recognition, and statistical machine learning algorithms to provide insight into data and address unmet needs (primarily in the healthcare domain). My algorithms are directly driven by and validated on complicated real-world problems, providing insight and facilitating interpretation of the underlying key mechanisms of the modeled system. My work is inherently multi-disciplinary, collaborating with industrial partners and colleagues from diverse backgrounds worldwide.

Indicative applications include longtidutinal telemonitoring of chronic diseases, natural language processing, data fusion, multi-sensor processing, biomechanics, and betting strategy optimisation. 


BSc Biomedical Engineering, Technological Educational Institute of Athens, Greece

BEng Electrical Engineering and Electronics, University of Liverpool, UK

MSc Signal Processing and Communications, Newcastle University, UK

DPhil (PhD) Applied Mathematics, University of Oxford, UK

FHEA: Fellow, Higher Education Academy


I studied Engineering for my undergraduate and masters degrees and completed a PhD in Applied Mathematics at the University of Oxford in September 2012. I have stayed at the University of Oxford to work as a research fellow in Biomedical Engineering and Applied Mathematics (2012-2016), Stipendiary Lecturer in Engineering Science (2014-2016) and Lecturer at the Said Business School (2016); since January 2017 I am a Chancellor’s Fellow in Data Science at the Usher Institute of Population Health and Informatics. 

I am the recipient of the National Greek State Scholarship (2003 & 2004) graduating as the top student in biomedical engineering in Athens, the BNFL prize for best undergraduate thesis at the University of Liverpool (2007), the CTA scholarship for my MSc at the University of Newcastle (2007), an Intel/EPSRC scholarship for my PhD at the University of Oxford (2008-2012), the student paper award at the NOLTA International meeting (2010), the Andrew Goudie award from St Cross College, University of Oxford, (2011), the EPSRC Doctoral Prize award (2012), the young scientist award at the international workshop MAVEBA (2013), and the EPSRC Statistics and Machine Learning award (2015). I was shortlisted in the final six candidates for the Papanikolaou prize (2011), and was part of the Oxford biomedical engineering team that won the annual Physionet/Computing in Cardiology Competition (2012) for “Predicting mortality of ICU patients”. I was an ‘Outstanding Reviewer’ for the journal Computers in Biology and Medicine (2015), and won a ‘Best reviewer award’ from the IEEE Journal of Biomedical Health Informatics (2015). 

Outside academics, I am a keen chess player having been the Greek under-20s champion (2003) and having participated in European and World Chess Championships.


Collaborative Activity

I have active collaborations with researchers worldwide. I have published papers/written grants with researchers based at the UK, USA, Australia, Spain, Greece, Italy, Chile, India.

Some of my collaborators are based at MIT, Stanford, University of Oxford, Polytecnica de Madrid, University of Melbourne.


I have taught undergraduate or graduate courses in the Engineering Science dept., the Mathematics dept., the Sleep and Circadian Neuroscience Institute, and the Said Business School, at the University of Oxford.

I have supervised two undergraduate projects, two MSc projects and four PhD students. Currently, I am supervising 6 PhD students in my research group: https://www.darth-group.com/


Current Research Interests

Time-series analysis, signal processing, pattern recognition, and statistical machine learning algorithms, focusing on biomedical applications.

Indicative projects I am currently working on include longitudinal telemonitoring of chronic diseases using wearable devices (e.g. smartwatches) and smartphones, and speech signal processing applications.

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